Computational Model Development of Drug-Target Interaction Prediction: A Review

In the medical field, drug-target interactions are very important for the diagnosis and treatment of diseases, they also can help researchers predict the link between biomolecules in the biological field, such as drug-protein and protein-target correlations. Therefore, the drug-target research is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, computational prediction methods for drug-target relationships are increasingly favored by researchers. In this review, we summarize several computational prediction models of the drug-target connections during the past two years, and briefly introduce their advantages and shortcomings. Finally, several further interesting research directions of drug-target interactions are listed.

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DeepPurpose: a deep learning library for drug–target interaction prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa1005 ◽

2020 ◽

Author(s):

Kexin Huang ◽

Tianfan Fu ◽

Lucas M Glass ◽

Marinka Zitnik ◽

Cao Xiao ◽

...

Keyword(s):

Deep Learning ◽

Drug Target ◽

Prediction Models ◽

State Of The Art ◽

Supplementary Information ◽

Target Interaction ◽

Interaction Prediction ◽

Computer Scientists ◽

Benchmark Datasets ◽

Biomedical Field

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

Current Drug Metabolism ◽

10.2174/1389200219666180821094047 ◽

2019 ◽

Vol 20 (3) ◽

pp. 194-202 ◽

Cited By ~ 14

Author(s):

Wen Zhang ◽

Weiran Lin ◽

Ding Zhang ◽

Siman Wang ◽

Jingwen Shi ◽

...

Keyword(s):

Machine Learning ◽

Computational Methods ◽

Drug Target ◽

Prediction Models ◽

Prediction Methods ◽

Crucial Issue ◽

Target Interaction ◽

Interaction Prediction ◽

Recent Advances ◽

Drug Similarity

Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

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A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

BMC Bioinformatics ◽

10.1186/s12859-020-03677-1 ◽

2020 ◽

Vol 21 (S13) ◽

Author(s):

Jiajie Peng ◽

Jingyi Li ◽

Xuequn Shang

Keyword(s):

Neural Network ◽

Drug Discovery ◽

Drug Target ◽

Deep Neural Network ◽

Computational Prediction ◽

Representation Learning ◽

Feature Representation ◽

New Drugs ◽

Target Interaction ◽

Interaction Prediction

Abstract Background Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. Results We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. Conclusions All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.

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DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations

Chemical Science ◽

10.1039/c9sc03414e ◽

2020 ◽

Vol 11 (9) ◽

pp. 2531-2557 ◽

Cited By ~ 14

Author(s):

Ahmet Sureyya Rifaioglu ◽

Esra Nalbat ◽

Volkan Atalay ◽

Maria Jesus Martin ◽

Rengul Cetin-Atalay ◽

...

Keyword(s):

Neural Networks ◽

Small Molecules ◽

Drug Target ◽

High Performance ◽

Prediction Models ◽

Drug Candidate ◽

Binding Properties ◽

Target Interaction ◽

Interaction Prediction ◽

Specific Prediction

The DEEPScreen system is composed of 704 target protein specific prediction models, each independently trained using experimental bioactivity measurements against many drug candidate small molecules, and optimized according to the binding properties of the target proteins.

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The Computational Models of Drug-target Interaction Prediction

Protein and Peptide Letters ◽

10.2174/0929866526666190410124110 ◽

2020 ◽

Vol 27 (5) ◽

pp. 348-358 ◽

Cited By ~ 3

Author(s):

Yijie Ding ◽

Jijun Tang ◽

Fei Guo

Keyword(s):

Machine Learning ◽

Computational Methods ◽

Drug Target ◽

Computational Models ◽

Data Sets ◽

Target Interaction ◽

Interaction Prediction ◽

The Past ◽

Benchmark Data ◽

Precision Recall Curve

:The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semisupervised models, have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR).

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